Anthropomorphism as Social Affordance: Charting the Co-Animation of Chatbots into Social “Agents”
Bibliographic record
Abstract
The mimesis of human traits exhibited by large language models (LLMs) has led some users to perceive these technical systems as agentic, capable of achieving reciprocal and seemingly human-like communication. These misperceptions have, in turn, been linked to documented harms in human-AI interactions (HAIs). This conceptual paper explores current interventions in response to interaction harms, taking AI companions as an illustrative example. We analyze documented cases of AI companion applications that have led to severe harms, including suicide, illustrating that current redressive approaches fail to account for the network of distributed human agents that collectively "animate" anthropomorphic features and encourage some users to regard AI systems as social "agents." By framing anthropomorphism as a social affordance that reproduces a broader distributed process spanning development, design, user interaction, socio-cultural contexts, and institutional forces, this paper demonstrates the necessity for distributed governance of anthropomorphic AI features across these diverse agentic forces. We proceed to discuss obstacles to appropriate governance, including power asymmetries between different agents, and outline existing models that could be adapted for more effective interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".